
import pandas as pd
#import data set
#view data
diabetes = pd.read_csv(r'I:\diabetes\delete数据\train.csv')
diabetes.info()
print(diabetes.describe())

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5641 entries, 0 to 5640
Data columns (total 40 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   sex                    5641 non-null   int64  
 1   age                    5641 non-null   int64  
 2   AST                    4421 non-null   float64
 3   ALT                    4421 non-null   float64
 4   ALP                    4421 non-null   float64
 5   GGT                    4421 non-null   float64
 6   TP                     4421 non-null   float64
 7   Alb                    4421 non-null   float64
 8   GLB                    4421 non-null   float64
 9   A/G                    4421 non-null   float64
 10  TG                     4423 non-null   float64
 11  TCHO                   4423 non-null   float64
 12  HDL-C                  4423 non-null   float64
 13  LDL-C                  4423 non-null   float64
 14  Urea                   4263 non-null   float64
 15  Cre                    4263 non-null   float64
 16  UA                     4263 non-null   float64
 17  HBsAg                  1363 non-null   float64
 18  Second liver antibody  1363 non-null   float64
 19  HBeAg                  1363 non-null   float64
 20  HBeAb                  1363 non-null   float64
 21  HBcAb                  1363 non-null   float64
 22  WBC                    5625 non-null   float64
 23  RBC                    5625 non-null   float64
 24  HGB                    5625 non-null   float64
 25  PCV                    5625 non-null   float64
 26  MCV                    5625 non-null   float64
 27  MCH                    5625 non-null   float64
 28  MCHC                   5625 non-null   float64
 29  RDW                    5625 non-null   float64
 30  PLT                    5625 non-null   float64
 31  MPV                    5618 non-null   float64
 32  PDW                    5618 non-null   float64
 33  PCT                    5618 non-null   float64
 34  NEUT%                  5625 non-null   float64
 35  LY%                    5625 non-null   float64
 36  MONO%                  5625 non-null   float64
 37  EOS%                   5625 non-null   float64
 38  BAS%                   5625 non-null   float64
 39  GLU                    5641 non-null   float64
dtypes: float64(38), int64(2)
memory usage: 1.7 MB
               sex          age          AST         ALT          ALP  \
count  5641.000000  5641.000000  4421.000000  4421.00000  4421.000000   
mean      0.489984    45.581280    26.805892    27.65202    87.482278   
std       0.499944    12.962952    13.563003    22.61152    25.630897   
min       0.000000     3.000000    10.040000     0.12000    22.980000   
25%       0.000000    35.000000    20.250000    15.13000    70.370000   
50%       0.000000    45.000000    23.890000    21.48000    84.470000   
75%       1.000000    54.000000    29.270000    32.30000   100.210000   
max       1.000000    93.000000   434.950000   498.89000   374.320000   

               GGT           TP          Alb          GLB          A/G  ...  \
count  4421.000000  4421.000000  4421.000000  4421.000000  4421.000000  ...   
mean     38.823762    76.771108    45.822504    30.948604     1.502538  ...   
std      40.788282     4.016055     2.617401     3.565997     0.220621  ...   
min       6.360000    57.320000    29.540000     7.060000     0.520000  ...   
25%      17.810000    74.190000    44.130000    28.570000     1.360000  ...   
50%      26.190000    76.630000    45.820000    30.780000     1.490000  ...   
75%      43.850000    79.530000    47.570000    33.180000     1.630000  ...   
max     736.990000   100.410000    54.080000    66.180000     7.120000  ...   

               PLT          MPV          PDW          PCT        NEUT%  \
count  5625.000000  5618.000000  5618.000000  5618.000000  5625.000000   
mean    253.082667    10.653346    13.304254     0.267901    56.717404   
std      59.591793     0.985545     2.173877     0.060507     7.800225   
min      37.000000     7.100000     8.000000     0.042000    14.400000   
25%     213.000000    10.000000    11.700000     0.230000    51.600000   
50%     249.000000    10.600000    12.900000     0.260000    56.700000   
75%     289.000000    11.300000    14.600000     0.300000    62.000000   
max     745.000000    15.200000    25.300000     0.710000    88.500000   

               LY%        MONO%         EOS%         BAS%          GLU  
count  5625.000000  5625.000000  5625.000000  5625.000000  5641.000000  
mean     33.779218     6.856978     2.043982     0.603698     5.626111  
std       7.253566     1.567647     1.710997     0.291786     1.481999  
min       7.500000     3.100000     0.000000     0.000000     3.070000  
25%      28.800000     5.800000     0.900000     0.400000     4.920000  
50%      33.600000     6.700000     1.600000     0.600000     5.290000  
75%      38.500000     7.700000     2.600000     0.700000     5.760000  
max      76.300000    23.200000    22.500000     3.500000    23.620000  

[8 rows x 40 columns]


pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
x = diabetes.drop("GLU", axis = 1)
x


y = diabetes[["GLU"]]
y


# interpolation
trains_interpo = diabetes.interpolate()
trains_interpo = trains_interpo.fillna(trains_interpo.mean())
trains_interpo
# sum after interpolation
print(trains_interpo.isnull().sum())

sex                      0
age                      0
AST                      0
ALT                      0
ALP                      0
GGT                      0
TP                       0
Alb                      0
GLB                      0
A/G                      0
TG                       0
TCHO                     0
HDL-C                    0
LDL-C                    0
Urea                     0
Cre                      0
UA                       0
HBsAg                    0
Second liver antibody    0
HBeAg                    0
HBeAb                    0
HBcAb                    0
WBC                      0
RBC                      0
HGB                      0
PCV                      0
MCV                      0
MCH                      0
MCHC                     0
RDW                      0
PLT                      0
MPV                      0
PDW                      0
PCT                      0
NEUT%                    0
LY%                      0
MONO%                    0
EOS%                     0
BAS%                     0
GLU                      0
dtype: int64


trains_interpo.info()
print(len(trains_interpo))

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5641 entries, 0 to 5640
Data columns (total 40 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   sex                    5641 non-null   int64  
 1   age                    5641 non-null   int64  
 2   AST                    5641 non-null   float64
 3   ALT                    5641 non-null   float64
 4   ALP                    5641 non-null   float64
 5   GGT                    5641 non-null   float64
 6   TP                     5641 non-null   float64
 7   Alb                    5641 non-null   float64
 8   GLB                    5641 non-null   float64
 9   A/G                    5641 non-null   float64
 10  TG                     5641 non-null   float64
 11  TCHO                   5641 non-null   float64
 12  HDL-C                  5641 non-null   float64
 13  LDL-C                  5641 non-null   float64
 14  Urea                   5641 non-null   float64
 15  Cre                    5641 non-null   float64
 16  UA                     5641 non-null   float64
 17  HBsAg                  5641 non-null   float64
 18  Second liver antibody  5641 non-null   float64
 19  HBeAg                  5641 non-null   float64
 20  HBeAb                  5641 non-null   float64
 21  HBcAb                  5641 non-null   float64
 22  WBC                    5641 non-null   float64
 23  RBC                    5641 non-null   float64
 24  HGB                    5641 non-null   float64
 25  PCV                    5641 non-null   float64
 26  MCV                    5641 non-null   float64
 27  MCH                    5641 non-null   float64
 28  MCHC                   5641 non-null   float64
 29  RDW                    5641 non-null   float64
 30  PLT                    5641 non-null   float64
 31  MPV                    5641 non-null   float64
 32  PDW                    5641 non-null   float64
 33  PCT                    5641 non-null   float64
 34  NEUT%                  5641 non-null   float64
 35  LY%                    5641 non-null   float64
 36  MONO%                  5641 non-null   float64
 37  EOS%                   5641 non-null   float64
 38  BAS%                   5641 non-null   float64
 39  GLU                    5641 non-null   float64
dtypes: float64(38), int64(2)
memory usage: 1.7 MB
5641


# pd.set_option('display.max_columns', None)
# pd.set_option('display.max_rows', None)
x_2 = trains_interpo.drop("GLU", axis = 1)
x_2


y_2 = trains_interpo[["GLU"]]
y_2


"""
feature engineering
from sklearn.preprocessing import StandardScaler

cols_x = ['sex','age','AST','ALT','ALP','GGT','TP','Alb','GLB','A/G','TG','TCHO','HDL-C','LDL-C','Urea','Cre','UA','HBsAg',
          'Second liver antibody','HBeAg','HBeAb','HBcAb','WBC','RBC','HGB','PCV','MCV','MCH','MCHC','RDW','PLT','MPV','PDW',
          'PCT','NEUT%','LY%','MONO%','EOS%','BAS%']
cols_y = ['GLU']
# 
ss = StandardScaler()
# 
x_1 = ss.fit_transform(x_1)
x_1 = pd.DataFrame(x_1, columns=cols_x)
# 
y_1 = ss.fit_transform(y_1)
y_1 = pd.DataFrame(y_1, columns=cols_y)
# 
trains_1 = pd.concat([x_1,y_1],axis=1)
trains_1
"""

"\nfrom sklearn.preprocessing import StandardScaler\n\ncols_x = ['sex','age','AST','ALT','ALP','GGT','TP','Alb','GLB','A/G','TG','TCHO','HDL-C','LDL-C','Urea','Cre','UA','HBsAg',\n          'Second liver antibody','HBeAg','HBeAb','HBcAb','WBC','RBC','HGB','PCV','MCV','MCH','MCHC','RDW','PLT','MPV','PDW',\n          'PCT','NEUT%','LY%','MONO%','EOS%','BAS%']\ncols_y = ['GLU']\n# 实例化\nss = StandardScaler()\n# 特征数据\nx_1 = ss.fit_transform(x_1)\nx_1 = pd.DataFrame(x_1, columns=cols_x)\n# 目标变量\ny_1 = ss.fit_transform(y_1)\ny_1 = pd.DataFrame(y_1, columns=cols_y)\n# 列拼接\ntrains_1 = pd.concat([x_1,y_1],axis=1)\ntrains_1\n"


# Histogram
import matplotlib.pyplot as plt
import seaborn as sns
import hvplot.pandas
%matplotlib inline

# Retina display
%config InlineBackend.figure_format = 'retina'

# plt.style.use('ggplot') 
plt.style.use("fivethirtyeight")

# Pandas Display three decimal 
pd.set_option('display.float_format', lambda x: '{:.3f}'.format(x))

for k in ['sex','age','AST','ALT','ALP','GGT','TP','Alb','GLB','A/G','TG','TCHO','HDL-C','LDL-C','Urea','Cre','UA','HBsAg',
          'Second liver antibody','HBeAg','HBeAb','HBcAb','WBC','RBC','HGB','PCV','MCV','MCH','MCHC','RDW','PLT','MPV','PDW',
          'PCT','NEUT%','LY%','MONO%','EOS%','BAS%','GLU']:
    plt.figure(figsize = (15,5))
    sns.barplot(x = trains_interpo[k]. value_counts().index, y = trains_interpo[k].value_counts().values)
    plt.title(k)

C:\Users\Xzt\AppData\Local\Temp\ipykernel_61932\3760752448.py:19: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.
  plt.figure(figsize = (15,5))


corr = trains_interpo.corr()
plt.figure(figsize = (25, 20))

sns.heatmap(corr,
           annot = True,
           fmt = ".1f",
#            cmap = "YlGn" 
           )
plt.show()


corr["GLU"].sort_values()

sex                     -0.152
RDW                     -0.080
PLT                     -0.078
PCT                     -0.070
HDL-C                   -0.057
LY%                     -0.053
Second liver antibody   -0.044
HBeAb                   -0.029
A/G                     -0.016
HBeAg                   -0.004
HBcAb                    0.000
MCV                      0.001
Alb                      0.005
EOS%                     0.006
MONO%                    0.009
HBsAg                    0.015
UA                       0.017
MPV                      0.024
BAS%                     0.026
PDW                      0.042
TP                       0.045
NEUT%                    0.046
GLB                      0.047
MCH                      0.075
Cre                      0.085
WBC                      0.091
AST                      0.092
GGT                      0.116
ALT                      0.118
RBC                      0.122
Urea                     0.124
PCV                      0.125
ALP                      0.128
MCHC                     0.149
LDL-C                    0.150
TCHO                     0.153
HGB                      0.153
TG                       0.237
age                      0.265
GLU                      1.000
Name: GLU, dtype: float64


sns.pairplot(trains_interpo[["age","TG","HGB","GLU"]]) # The first three characteristics of absolute value
plt.show()


# modelling
import sklearn
from sklearn.linear_model import LinearRegression
# Model instantiation
le = LinearRegression()
# process of fit
le.fit(x_2, y_2)
# acquire Regression coefficient
coef2 = le.coef_ #39 Regression coefficient
coef2

array([[-3.19603546e-01,  2.88476554e-02, -9.95652179e-03,
         9.97639049e-03,  1.79720062e-03,  4.99151611e-04,
         1.10373429e-02, -8.11689094e-03,  1.91542339e-02,
         2.21619532e-01,  1.53775627e-01,  1.89202208e-02,
        -4.14561697e-02,  4.27662477e-02,  7.63845042e-02,
        -7.05346128e-04, -2.26284660e-03,  3.47894051e-03,
        -6.26377579e-03, -2.51952318e-02, -2.03475917e-02,
        -6.51116960e-04,  5.48087758e-02,  1.00585332e+00,
         1.01096868e-02, -1.53180731e+01, -6.14329136e-02,
         2.25192668e-01, -1.53678561e-02, -1.53318351e-01,
        -2.89519133e-03, -5.18099775e-02,  2.34306104e-02,
         2.17388059e+00,  1.05156362e+00,  1.04513340e+00,
         1.05207280e+00,  1.03542290e+00,  1.14361826e+00]])


# import test
testA = pd.read_csv(r'I:\diabetes\delete数据\testA.csv')
testA.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 39 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   sex                    1000 non-null   int64  
 1   age                    1000 non-null   int64  
 2   AST                    815 non-null    float64
 3   ALT                    815 non-null    float64
 4   ALP                    815 non-null    float64
 5   GGT                    815 non-null    float64
 6   TP                     815 non-null    float64
 7   Alb                    815 non-null    float64
 8   GLB                    815 non-null    float64
 9   A/G                    815 non-null    float64
 10  TG                     824 non-null    float64
 11  TCHO                   824 non-null    float64
 12  HDL-C                  824 non-null    float64
 13  LDL-C                  824 non-null    float64
 14  Urea                   806 non-null    float64
 15  Cre                    806 non-null    float64
 16  UA                     806 non-null    float64
 17  HBsAg                  169 non-null    float64
 18  Second liver antibody  169 non-null    float64
 19  HBeAg                  169 non-null    float64
 20  HBeAb                  169 non-null    float64
 21  HBcAb                  169 non-null    float64
 22  WBC                    995 non-null    float64
 23  RBC                    995 non-null    float64
 24  HGB                    995 non-null    float64
 25  PCV                    995 non-null    float64
 26  MCV                    995 non-null    float64
 27  MCH                    995 non-null    float64
 28  MCHC                   995 non-null    float64
 29  RDW                    995 non-null    float64
 30  PLT                    995 non-null    float64
 31  MPV                    994 non-null    float64
 32  PDW                    994 non-null    float64
 33  PCT                    994 non-null    float64
 34  NEUT%                  995 non-null    float64
 35  LY%                    995 non-null    float64
 36  MONO%                  995 non-null    float64
 37  EOS%                   995 non-null    float64
 38  BAS%                   995 non-null    float64
dtypes: float64(37), int64(2)
memory usage: 304.8 KB


# interpolation of test
test_del = testA.interpolate()
test_del = test_del.fillna(test_del.mean())
test_del


# prediction of test
predict_del = le.predict(test_del)
predict_del

array([[6.26594384],
       [6.18072393],
       [5.60008881],
       [6.05059836],
       [4.90484873],
       [5.07422641],
       [4.7839599 ],
       [5.44940213],
       [5.87373624],
       [6.01928278],
       [4.83931302],
       [6.04683948],
       [6.06793098],
       [5.745193  ],
       [6.24871772],
       [3.49079996],
       [5.18448373],
       [6.3130797 ],
       [5.11715309],
       [4.72186796],
       [5.56692803],
       [5.50265499],
       [6.42496977],
       [6.37565246],
       [5.48239831],
       [5.61356228],
       [5.23946197],
       [5.44287589],
       [5.60893606],
       [5.3436533 ],
       [5.14914031],
       [5.63324445],
       [5.23305784],
       [7.29319159],
       [5.70077855],
       [4.86822299],
       [5.39011266],
       [5.02733146],
       [5.63720056],
       [5.28031701],
       [5.87833371],
       [5.30339143],
       [5.40726323],
       [5.69504791],
       [6.02985619],
       [6.27608729],
       [6.11040263],
       [5.07481865],
       [4.47528562],
       [5.73397971],
       [4.74432502],
       [4.62051028],
       [4.51128732],
       [4.94566037],
       [4.59653056],
       [4.27917874],
       [5.10606098],
       [4.75178758],
       [4.47401434],
       [5.01976095],
       [5.05176628],
       [5.15788196],
       [7.54809078],
       [4.81631384],
       [4.56643265],
       [4.97832242],
       [4.91227367],
       [4.31101239],
       [5.71158593],
       [5.79884529],
       [6.39662296],
       [5.6770694 ],
       [6.25539954],
       [5.24663133],
       [5.94834565],
       [5.29462646],
       [5.70037119],
       [5.3822895 ],
       [6.62784188],
       [6.32864448],
       [6.24445152],
       [5.79930378],
       [5.01608928],
       [6.00288514],
       [6.13272406],
       [5.91704593],
       [6.17699058],
       [5.59266389],
       [5.99001889],
       [5.77035491],
       [5.73941535],
       [6.23172512],
       [6.07901722],
       [5.25337119],
       [5.95473852],
       [5.86801963],
       [5.40534738],
       [5.45303079],
       [6.29552153],
       [5.67563142],
       [5.80952664],
       [5.14338153],
       [6.48850982],
       [5.81652946],
       [5.90824723],
       [5.74770322],
       [5.2111296 ],
       [5.43642837],
       [5.9077155 ],
       [6.6250724 ],
       [6.28033712],
       [5.96931221],
       [5.86339033],
       [5.72032198],
       [6.13694789],
       [4.96085589],
       [5.59046859],
       [5.93674626],
       [5.50629757],
       [4.98331156],
       [6.56216034],
       [4.99771419],
       [5.33812191],
       [5.97251848],
       [5.1727253 ],
       [5.85862885],
       [4.70528422],
       [4.68907115],
       [5.98307059],
       [5.27891302],
       [5.92651946],
       [6.09901322],
       [5.92751199],
       [5.85203736],
       [5.46271976],
       [5.95260027],
       [5.51767347],
       [5.34201151],
       [5.97226955],
       [5.32593408],
       [5.31265   ],
       [5.51583935],
       [6.32211076],
       [6.08827266],
       [7.14148618],
       [4.64712327],
       [6.57719646],
       [5.49498999],
       [5.66099873],
       [4.52699087],
       [4.1641328 ],
       [5.3330128 ],
       [6.26650367],
       [6.67736655],
       [5.12954135],
       [5.77018044],
       [6.12317118],
       [6.39158741],
       [5.64476409],
       [5.77752244],
       [5.56307253],
       [6.21168458],
       [6.30117512],
       [4.99290972],
       [4.93127788],
       [6.139731  ],
       [5.43123015],
       [5.6609835 ],
       [5.97827563],
       [6.83467836],
       [5.63835184],
       [5.20672018],
       [5.80222923],
       [6.65240581],
       [4.92523494],
       [5.7450143 ],
       [5.29268609],
       [6.26033406],
       [6.33140825],
       [5.87554801],
       [5.91208835],
       [5.23451969],
       [5.46364618],
       [5.72077491],
       [4.60164973],
       [6.22761206],
       [4.95715107],
       [5.62260196],
       [5.69714677],
       [5.25728029],
       [5.56560892],
       [5.28173072],
       [6.20156544],
       [6.30237731],
       [5.6304939 ],
       [5.22417333],
       [5.34879336],
       [6.12448828],
       [4.55495598],
       [6.37083602],
       [5.26219092],
       [4.08917157],
       [4.76323376],
       [6.02610491],
       [5.07312909],
       [5.70636697],
       [5.78800215],
       [7.25068834],
       [6.42825489],
       [4.94257638],
       [5.09993826],
       [6.51322338],
       [5.6346408 ],
       [4.93981047],
       [5.73628657],
       [6.11713106],
       [6.12115206],
       [6.08165556],
       [6.21451739],
       [6.75198186],
       [6.82462398],
       [5.44647356],
       [6.10385712],
       [5.64953394],
       [5.81496864],
       [4.43617403],
       [5.66937776],
       [5.82187446],
       [5.25315617],
       [4.94315013],
       [4.38759265],
       [4.602406  ],
       [4.59833608],
       [6.20680272],
       [4.72118775],
       [6.0961356 ],
       [6.27124102],
       [6.22576254],
       [5.91633679],
       [6.08836065],
       [6.08178508],
       [6.5512726 ],
       [6.6424003 ],
       [4.7462294 ],
       [6.2176764 ],
       [6.8677294 ],
       [6.44268134],
       [7.33618235],
       [5.38859999],
       [6.43798735],
       [5.67378059],
       [5.3340173 ],
       [6.30156722],
       [6.80137803],
       [6.64380336],
       [5.46681149],
       [5.25182848],
       [5.49480637],
       [5.41221922],
       [4.77532841],
       [5.31899686],
       [4.95128454],
       [5.74946545],
       [5.15340971],
       [6.71554656],
       [6.61407375],
       [6.00716708],
       [6.59131245],
       [6.43533348],
       [5.49851013],
       [5.01536981],
       [5.31380007],
       [6.05883803],
       [5.299863  ],
       [6.18560092],
       [5.44503843],
       [6.45252437],
       [6.08209456],
       [5.87604277],
       [4.7524812 ],
       [5.66363318],
       [5.91228196],
       [5.96439835],
       [5.89394959],
       [5.6567275 ],
       [5.51736045],
       [6.21808133],
       [5.87648573],
       [5.40868915],
       [6.01076366],
       [5.78945877],
       [5.73057922],
       [7.07676662],
       [6.54752773],
       [4.48897667],
       [5.2968904 ],
       [6.0582789 ],
       [5.50631494],
       [5.25594647],
       [5.3196967 ],
       [6.15703528],
       [4.91918433],
       [5.68870577],
       [6.63622837],
       [5.27577226],
       [5.85245671],
       [6.55667866],
       [5.47393714],
       [5.61106521],
       [5.44329405],
       [6.21453729],
       [6.07921719],
       [4.69482839],
       [7.80845612],
       [4.91114617],
       [5.44227824],
       [5.27113375],
       [5.59253411],
       [6.43566513],
       [6.13610815],
       [5.90193026],
       [5.40656582],
       [5.59563799],
       [5.21103674],
       [6.09763193],
       [5.95402702],
       [6.26740865],
       [5.02697018],
       [5.62271282],
       [6.35530125],
       [6.90693907],
       [6.26309658],
       [5.48593374],
       [6.34102668],
       [5.84535658],
       [5.71037526],
       [6.04945802],
       [5.92095629],
       [5.50944548],
       [6.67452398],
       [5.93215062],
       [5.52905412],
       [4.73052025],
       [5.20394271],
       [5.12302743],
       [5.45559212],
       [5.65830979],
       [5.47091495],
       [5.04944366],
       [6.28742675],
       [4.69674913],
       [4.8505017 ],
       [5.51413246],
       [5.58452238],
       [5.47751369],
       [5.6339889 ],
       [5.07350732],
       [5.25461755],
       [4.92975138],
       [5.69009866],
       [5.48576515],
       [6.0011506 ],
       [4.82581084],
       [5.59937394],
       [6.59120388],
       [6.21168395],
       [5.84207833],
       [5.05001703],
       [6.27326851],
       [5.75714332],
       [5.67056197],
       [5.88589894],
       [5.98646402],
       [4.81787031],
       [6.60059753],
       [4.46619638],
       [5.83521936],
       [5.58729577],
       [6.43551395],
       [5.73402955],
       [5.15329056],
       [5.68321753],
       [5.07994448],
       [5.79975239],
       [5.35921904],
       [5.65493725],
       [6.34057332],
       [6.39630387],
       [5.43378292],
       [4.94863796],
       [6.04425315],
       [5.93439123],
       [5.98804153],
       [4.73660216],
       [6.16836781],
       [5.8306739 ],
       [5.96939016],
       [5.45800532],
       [6.23995132],
       [4.92426234],
       [5.58056636],
       [5.93609639],
       [5.2408989 ],
       [6.66206458],
       [4.82909552],
       [6.07880218],
       [5.70354344],
       [6.08293289],
       [6.75613691],
       [5.82695486],
       [6.65998804],
       [5.55237106],
       [5.42795904],
       [7.07930163],
       [5.36279905],
       [5.91103914],
       [5.03575191],
       [5.81541916],
       [5.64355573],
       [6.1261361 ],
       [5.69040918],
       [5.51715302],
       [6.33701646],
       [5.45051106],
       [5.12266742],
       [5.53632439],
       [5.19060544],
       [5.41806514],
       [5.02657264],
       [5.66663176],
       [4.4621877 ],
       [5.29668367],
       [6.0850439 ],
       [6.09150712],
       [6.78971287],
       [6.32597799],
       [6.4618374 ],
       [5.3369326 ],
       [6.27242638],
       [5.68943425],
       [5.50593411],
       [5.75787113],
       [6.02281488],
       [5.78098608],
       [5.87249032],
       [5.82867458],
       [6.18453018],
       [5.99035794],
       [5.47535763],
       [5.52166964],
       [5.69967204],
       [6.17640993],
       [5.5764584 ],
       [5.99310824],
       [5.68030992],
       [6.15960218],
       [5.89780955],
       [5.49777354],
       [5.71488209],
       [6.31422123],
       [6.30292336],
       [5.84969834],
       [5.48282295],
       [5.29598223],
       [5.87510476],
       [5.73078867],
       [6.08690409],
       [5.29026488],
       [5.66980411],
       [5.47742854],
       [5.56701696],
       [5.10595952],
       [5.46784006],
       [5.42466444],
       [5.59958183],
       [5.525101  ],
       [5.53004112],
       [5.14231984],
       [5.4861469 ],
       [4.63230727],
       [5.6261413 ],
       [5.66197732],
       [4.89983727],
       [4.89043398],
       [5.32124932],
       [5.51784102],
       [5.31135619],
       [5.15857671],
       [5.3394183 ],
       [5.33931765],
       [5.26207921],
       [4.84754768],
       [5.34001957],
       [5.4158249 ],
       [5.28858379],
       [4.49477753],
       [4.92721919],
       [5.17383082],
       [5.04057573],
       [4.85279611],
       [5.27103177],
       [5.07170932],
       [5.34410817],
       [5.56530958],
       [5.35395845],
       [5.45450728],
       [5.35453998],
       [4.71297001],
       [5.20898551],
       [5.06625444],
       [5.35923557],
       [5.50476218],
       [5.78949324],
       [5.54711756],
       [5.70354381],
       [5.49060079],
       [4.95183402],
       [6.07050007],
       [5.26585099],
       [5.10604068],
       [5.6077621 ],
       [5.09238617],
       [5.42604224],
       [5.27631127],
       [5.49568741],
       [5.10018613],
       [5.3848586 ],
       [5.41830779],
       [4.73513765],
       [5.08101251],
       [5.198554  ],
       [5.00774178],
       [5.31875488],
       [5.00955784],
       [5.46399138],
       [5.39595037],
       [4.76297129],
       [4.95732077],
       [4.41137155],
       [4.83670224],
       [5.27348158],
       [5.04161558],
       [6.1014209 ],
       [5.00060345],
       [5.97713591],
       [5.75097976],
       [5.09984928],
       [5.98348177],
       [5.26531648],
       [5.99507824],
       [5.61762882],
       [6.32497635],
       [5.55899857],
       [5.37188566],
       [5.5110359 ],
       [5.91956679],
       [5.52812066],
       [5.70503593],
       [5.13650773],
       [4.6086629 ],
       [5.86748   ],
       [6.69409599],
       [5.89478279],
       [5.71570365],
       [6.64374287],
       [6.15778967],
       [4.860474  ],
       [5.54461995],
       [5.79572871],
       [6.65196792],
       [4.92057262],
       [6.29446011],
       [5.54484171],
       [4.6237432 ],
       [5.38551442],
       [4.63650557],
       [5.77217598],
       [5.13536232],
       [5.59385767],
       [6.1981271 ],
       [5.10049551],
       [4.78562072],
       [5.86998338],
       [4.92478571],
       [5.74037763],
       [5.52020073],
       [5.48337662],
       [5.38520107],
       [6.04189573],
       [6.40832846],
       [6.23619417],
       [6.04772547],
       [6.25445373],
       [6.34719534],
       [4.89701493],
       [5.6657794 ],
       [5.83552259],
       [5.02006184],
       [5.60125842],
       [4.8909827 ],
       [6.04616675],
       [6.83431895],
       [7.35210348],
       [5.0711274 ],
       [6.54105492],
       [5.62502705],
       [6.29954226],
       [6.26227821],
       [6.33688773],
       [5.91675601],
       [5.0801935 ],
       [5.99357487],
       [5.11933675],
       [5.80214642],
       [4.75164886],
       [6.02292902],
       [4.78331149],
       [6.00586104],
       [4.79950792],
       [5.91422398],
       [4.82955728],
       [6.74838906],
       [6.07125906],
       [5.87093443],
       [5.3125179 ],
       [5.76662249],
       [6.46424368],
       [5.87383838],
       [7.29074804],
       [5.5903865 ],
       [5.33151246],
       [6.48483575],
       [4.94195162],
       [5.24074014],
       [5.72250549],
       [5.27100475],
       [5.92184729],
       [5.24875731],
       [5.96634079],
       [5.56698946],
       [6.10967129],
       [5.91668308],
       [5.41012431],
       [6.76648836],
       [5.09990786],
       [5.56266247],
       [6.38752648],
       [6.32539064],
       [6.47228898],
       [6.24178648],
       [4.49116004],
       [5.96703485],
       [5.74615108],
       [5.03876715],
       [5.38747849],
       [5.74961055],
       [6.27085309],
       [5.04192066],
       [5.03646474],
       [5.337861  ],
       [5.63738443],
       [6.04731089],
       [4.81586082],
       [5.72570737],
       [4.91364141],
       [5.40045085],
       [5.58103395],
       [6.18589097],
       [6.16967162],
       [5.91172583],
       [5.44670048],
       [4.68111395],
       [5.91512263],
       [5.11567285],
       [5.9306699 ],
       [5.87008731],
       [5.93641134],
       [4.93870697],
       [6.22335876],
       [6.06297801],
       [5.14557982],
       [5.19765076],
       [5.00522291],
       [4.74546897],
       [6.46900191],
       [5.97766993],
       [5.78963335],
       [5.93597706],
       [5.68618352],
       [5.06161928],
       [4.69420489],
       [5.59884836],
       [5.26754262],
       [6.15731249],
       [5.09365178],
       [4.31084141],
       [8.17613113],
       [6.44963033],
       [5.07393817],
       [6.13210461],
       [4.56305383],
       [5.16820164],
       [5.1610725 ],
       [5.55118767],
       [6.0015116 ],
       [5.37339765],
       [4.70114148],
       [4.68803292],
       [4.5604818 ],
       [5.15136337],
       [5.37572746],
       [5.25875316],
       [4.60786127],
       [5.8342545 ],
       [6.06611603],
       [6.59069083],
       [5.3548887 ],
       [6.01730055],
       [4.27419496],
       [6.08377361],
       [5.71866946],
       [5.97132614],
       [6.94449066],
       [5.20429389],
       [6.11939627],
       [4.83489005],
       [6.26639034],
       [6.13247512],
       [4.89957157],
       [5.35486444],
       [5.74675898],
       [5.52878849],
       [5.83689823],
       [6.34370873],
       [5.86160435],
       [5.71230683],
       [6.58097798],
       [6.07822983],
       [5.78914225],
       [5.5791678 ],
       [6.12135991],
       [4.9731733 ],
       [4.91312029],
       [5.12121615],
       [6.50145735],
       [6.88110564],
       [5.48630944],
       [5.75713777],
       [5.88624775],
       [5.57852563],
       [5.48318502],
       [5.08005381],
       [5.18226281],
       [5.2445981 ],
       [5.03993996],
       [4.78069874],
       [4.43325402],
       [4.82044682],
       [5.61582011],
       [5.31175323],
       [5.01412152],
       [4.91126062],
       [6.25217121],
       [6.44971698],
       [6.10514651],
       [5.5574911 ],
       [4.64202865],
       [5.62679409],
       [5.68296049],
       [5.7380696 ],
       [5.82357521],
       [5.91102426],
       [5.44988024],
       [6.53596433],
       [5.92642893],
       [5.08463437],
       [5.65435338],
       [6.16904401],
       [5.69552521],
       [4.68684021],
       [6.45268366],
       [5.9454651 ],
       [6.10418421],
       [5.11633532],
       [5.55815304],
       [5.53617884],
       [6.35944995],
       [5.07517599],
       [6.86555263],
       [5.92265406],
       [5.60218723],
       [6.18909924],
       [5.87737477],
       [5.91886839],
       [6.59978635],
       [6.17877428],
       [5.65911608],
       [6.04005764],
       [5.42752525],
       [6.51313908],
       [5.80112357],
       [5.08304217],
       [4.90119558],
       [5.62596621],
       [4.66470709],
       [6.15212161],
       [5.68538154],
       [5.21212311],
       [6.25855738],
       [5.68872766],
       [5.60698916],
       [5.85733778],
       [5.95125868],
       [4.85983278],
       [5.48718276],
       [6.15368963],
       [5.45244206],
       [6.11583245],
       [6.13857633],
       [5.94472269],
       [5.45678714],
       [6.17808138],
       [8.08438661],
       [6.64313146],
       [5.18066903],
       [6.15614335],
       [5.15392903],
       [6.19210381],
       [5.53447054],
       [6.00020392],
       [5.71751826],
       [5.22208256],
       [6.14432451],
       [5.21057769],
       [5.62095276],
       [5.34323823],
       [5.22410248],
       [5.30816008],
       [5.48908959],
       [6.0462233 ],
       [6.37836669],
       [6.07564012],
       [5.11324061],
       [4.77871882],
       [6.76629169],
       [5.71544621],
       [8.2214042 ],
       [4.96539898],
       [5.65397361],
       [5.47293083],
       [5.59493432],
       [4.94391721],
       [4.77643016],
       [5.72103781],
       [5.39690214],
       [5.83960274],
       [5.91934508],
       [5.44487564],
       [6.52903957],
       [6.07904525],
       [5.55780165],
       [5.76798169],
       [5.58600091],
       [5.45687257],
       [5.27478585],
       [5.21924858],
       [5.7399702 ],
       [5.23706214],
       [5.28612664],
       [5.23700221],
       [5.29694356],
       [6.32245884],
       [5.07602753],
       [5.80039815],
       [5.64800986],
       [5.59460041],
       [5.75312768],
       [5.27108536],
       [4.71267832],
       [5.42139867],
       [5.42355847],
       [5.96486821],
       [6.23175103],
       [6.12610946],
       [5.25527455],
       [5.72810126],
       [5.12786583],
       [5.62772918],
       [5.41768994],
       [4.68253585],
       [6.13272974],
       [6.10855145],
       [5.82083775],
       [5.56742151],
       [5.05980748],
       [5.02081533],
       [6.04115796],
       [5.97424252],
       [6.62285131],
       [4.94252396],
       [5.67148378],
       [5.34825516],
       [5.70786316],
       [6.00766107],
       [6.29588225],
       [6.43420072],
       [5.29954684],
       [4.7185805 ],
       [5.35317236],
       [5.64880236],
       [5.77829009],
       [6.03728919],
       [5.52618459],
       [5.95454433],
       [7.1535875 ],
       [5.90904598],
       [6.07077355],
       [5.90608469],
       [5.59202813],
       [5.2376155 ],
       [5.04367962],
       [6.47939844],
       [5.26629543],
       [6.4153731 ],
       [6.09057602],
       [4.63956246],
       [5.59243612],
       [6.25700902],
       [8.7571261 ],
       [5.80193213],
       [6.8836304 ],
       [6.01040536],
       [6.16554826],
       [6.14940198],
       [5.75690298],
       [6.0014148 ],
       [4.76428032],
       [5.99978391],
       [7.98708586],
       [5.15358283],
       [6.51925628],
       [5.70282821],
       [5.08139086],
       [4.98799963],
       [5.43100313],
       [6.11759663],
       [4.78594984],
       [4.96605133],
       [4.86836324],
       [4.69236695],
       [5.86476184],
       [8.72474215],
       [6.16300737],
       [7.50181839],
       [6.06175984],
       [5.47821248],
       [5.28587322],
       [6.24902617],
       [6.224769  ],
       [7.1180857 ],
       [5.93093305],
       [6.31099897],
       [5.51942802],
       [5.31777016],
       [5.55676087],
       [5.12657183],
       [4.63781072],
       [4.86917569],
       [6.60635474],
       [5.58837362],
       [6.31068979],
       [6.62223633],
       [6.16834943],
       [6.98166171],
       [5.89995093],
       [5.84851525],
       [5.97412124],
       [5.53589407],
       [5.61385037],
       [4.75714255],
       [6.25978825],
       [4.78296679],
       [5.93942775],
       [5.70131595],
       [6.57062092],
       [6.10732171],
       [5.41022349],
       [5.83608465],
       [5.92774621],
       [6.09900474],
       [5.95580406],
       [5.18068277],
       [5.14998005],
       [6.66418679],
       [6.53046017],
       [5.60036733],
       [6.26900893],
       [6.94387825],
       [5.01356671],
       [5.47074194]])


# import test answer
test_answer = pd.read_csv(r'I:\diabetes\delete数据\answerA.csv')
test_answer.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 1 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   GLU     1000 non-null   float64
dtypes: float64(1)
memory usage: 7.9 KB


import numpy as np
from sklearn.metrics import mean_squared_error

# 得分
print("Score:", le.score(test_del, test_answer))
print("RSME:", np.sqrt(mean_squared_error(test_answer, predict_del)))

Score: 0.13822047822315164
RSME: 1.3441552503917458


from numpy import array

coef2

# results

le_df = pd.DataFrame()

le_df["name"] = test_del.columns.tolist()
le_df["coef"] = coef2.reshape(-1,1)

le_df


# compared answer and prediction
test_del = pd.DataFrame({"answer": test_answer["GLU"].tolist(),
                         "predict": predict_del.flatten()
                        })
test_del


# compared plot
test_del.plot(figsize=(18,10))
plt.show()


# answer > prediciton
len(test_del.query("answer > predict")) / len(test_del)

0.442


# evaluation of model
plt.scatter(test_answer, predict_del, label="test")
plt.plot([test_answer.min(), test_answer.max()],
         [test_answer.min(), test_answer.max()],
         'k--',
         lw=3,
         label="predict"
        )

plt.show()

	name	coef
0	sex	-0.320
1	age	0.029
2	AST	-0.010
3	ALT	0.010
4	ALP	0.002
5	GGT	0.000
6	TP	0.011
7	Alb	-0.008
8	GLB	0.019
9	A/G	0.222
10	TG	0.154
11	TCHO	0.019
12	HDL-C	-0.041
13	LDL-C	0.043
14	Urea	0.076
15	Cre	-0.001
16	UA	-0.002
17	HBsAg	0.003
18	Second liver antibody	-0.006
19	HBeAg	-0.025
20	HBeAb	-0.020
21	HBcAb	-0.001
22	WBC	0.055
23	RBC	1.006
24	HGB	0.010
25	PCV	-15.318
26	MCV	-0.061
27	MCH	0.225
28	MCHC	-0.015
29	RDW	-0.153
30	PLT	-0.003
31	MPV	-0.052
32	PDW	0.023
33	PCT	2.174
34	NEUT%	1.052
35	LY%	1.045
36	MONO%	1.052
37	EOS%	1.035
38	BAS%	1.144

	answer	predict
0	5.500	6.266
1	5.310	6.181
2	5.050	5.600
3	7.230	6.051
4	5.930	4.905
5	5.530	5.074
6	4.570	4.784
7	5.990	5.449
8	5.500	5.874
9	6.250	6.019
10	5.000	4.839
11	5.470	6.047
12	5.120	6.068
13	5.350	5.745
14	7.320	6.249
15	5.610	3.491
16	5.040	5.184
17	6.400	6.313
18	5.600	5.117
19	5.610	4.722
20	5.580	5.567
21	6.110	5.503
22	5.800	6.425
23	6.330	6.376
24	5.480	5.482
25	4.670	5.614
26	5.570	5.239
27	5.770	5.443
28	5.860	5.609
29	5.520	5.344
30	5.060	5.149
31	4.860	5.633
32	5.330	5.233
33	5.810	7.293
34	5.140	5.701
35	5.910	4.868
36	5.650	5.390
37	5.100	5.027
38	4.710	5.637
39	5.580	5.280
40	4.520	5.878
41	7.070	5.303
42	5.430	5.407
43	5.680	5.695
44	5.590	6.030
45	5.690	6.276
46	14.040	6.110
47	5.300	5.075
48	5.030	4.475
49	6.330	5.734
50	5.160	4.744
51	6.600	4.621
52	4.810	4.511
53	5.550	4.946
54	4.810	4.597
55	4.390	4.279
56	4.610	5.106
57	5.130	4.752
58	5.350	4.474
59	4.810	5.020
60	5.360	5.052
61	5.030	5.158
62	6.340	7.548
63	5.400	4.816
64	4.720	4.566
65	5.400	4.978
66	5.310	4.912
67	5.130	4.311
68	5.520	5.712
69	4.910	5.799
70	9.760	6.397
71	5.690	5.677
72	4.400	6.255
73	4.510	5.247
74	5.200	5.948
75	4.560	5.295
76	5.210	5.700
77	5.570	5.382
78	6.880	6.628
79	5.670	6.329
80	5.680	6.244
81	5.020	5.799
82	6.210	5.016
83	4.980	6.003
84	5.570	6.133
85	5.160	5.917
86	5.230	6.177
87	5.430	5.593
88	5.710	5.990
89	5.770	5.770
90	4.950	5.739
91	6.250	6.232
92	6.720	6.079
93	8.410	5.253
94	5.470	5.955
95	5.300	5.868
96	5.980	5.405
97	6.020	5.453
98	6.460	6.296
99	6.630	5.676
100	5.550	5.810
101	5.900	5.143
102	10.160	6.489
103	8.610	5.817
104	9.100	5.908
105	4.920	5.748
106	5.550	5.211
107	4.640	5.436
108	4.800	5.908
109	6.240	6.625
110	5.960	6.280
111	5.130	5.969
112	5.980	5.863
113	6.140	5.720
114	5.210	6.137
115	6.020	4.961
116	5.390	5.590
117	5.080	5.937
118	5.840	5.506
119	4.660	4.983
120	6.010	6.562
121	4.870	4.998
122	5.290	5.338
123	5.550	5.973
124	5.130	5.173
125	6.380	5.859
126	5.130	4.705
127	4.330	4.689
128	5.400	5.983
129	4.640	5.279
130	5.260	5.927
131	5.250	6.099
132	5.110	5.928
133	5.820	5.852
134	5.170	5.463
135	5.420	5.953
136	4.870	5.518
137	6.120	5.342
138	6.110	5.972
139	6.950	5.326
140	5.340	5.313
141	5.350	5.516
142	6.250	6.322
143	6.630	6.088
144	5.300	7.141
145	4.640	4.647
146	5.920	6.577
147	4.760	5.495
148	5.790	5.661
149	4.370	4.527
150	7.160	4.164
151	5.010	5.333
152	5.300	6.267
153	5.260	6.677
154	5.460	5.130
155	4.850	5.770
156	4.990	6.123
157	7.270	6.392
158	5.700	5.645
159	4.740	5.778
160	5.030	5.563
161	6.340	6.212
162	5.720	6.301
163	5.500	4.993
164	5.600	4.931
165	6.110	6.140
166	4.920	5.431
167	5.240	5.661
168	4.760	5.978
169	5.410	6.835
170	6.160	5.638
171	5.300	5.207
172	5.390	5.802
173	6.090	6.652
174	5.560	4.925
175	5.630	5.745
176	4.890	5.293
177	5.490	6.260
178	4.670	6.331
179	5.000	5.876
180	5.980	5.912
181	5.490	5.235
182	5.010	5.464
183	5.220	5.721
184	5.510	4.602
185	5.290	6.228
186	5.120	4.957
187	6.270	5.623
188	5.360	5.697
189	4.970	5.257
190	4.630	5.566
191	5.220	5.282
192	7.780	6.202
193	5.280	6.302
194	5.160	5.630
195	5.370	5.224
196	5.270	5.349
197	6.410	6.124
198	5.630	4.555
199	10.020	6.371
200	5.150	5.262
201	5.690	4.089
202	5.170	4.763
203	7.280	6.026
204	5.080	5.073
205	5.510	5.706
206	5.890	5.788
207	6.210	7.251
208	6.440	6.428
209	5.130	4.943
210	5.220	5.100
211	4.870	6.513
212	4.790	5.635
213	5.000	4.940
214	5.980	5.736
215	5.330	6.117
216	6.170	6.121
217	5.750	6.082
218	8.210	6.215
219	5.390	6.752
220	7.300	6.825
221	4.720	5.446
222	4.460	6.104
223	4.420	5.650
224	5.560	5.815
225	5.730	4.436
226	4.780	5.669
227	6.000	5.822
228	5.670	5.253
229	4.740	4.943
230	5.540	4.388
231	4.660	4.602
232	4.870	4.598
233	5.820	6.207
234	5.020	4.721
235	6.810	6.096
236	4.970	6.271
237	5.870	6.226
238	5.360	5.916
239	5.250	6.088
240	6.170	6.082
241	7.750	6.551
242	5.350	6.642
243	5.600	4.746
244	6.220	6.218
245	10.630	6.868
246	6.200	6.443
247	5.680	7.336
248	5.250	5.389
249	6.930	6.438
250	6.350	5.674
251	4.910	5.334
252	6.710	6.302
253	5.930	6.801
254	7.280	6.644
255	5.150	5.467
256	5.130	5.252
257	5.820	5.495
258	9.540	5.412
259	4.780	4.775
260	5.750	5.319
261	5.420	4.951
262	6.290	5.749
263	5.100	5.153
264	5.280	6.716
265	5.820	6.614
266	6.980	6.007
267	14.530	6.591
268	5.720	6.435
269	5.120	5.499
270	5.250	5.015
271	5.980	5.314
272	6.570	6.059
273	4.840	5.300
274	5.520	6.186
275	6.070	5.445
276	7.330	6.453
277	5.500	6.082
278	5.550	5.876
279	5.260	4.752
280	4.940	5.664
281	6.220	5.912
282	5.720	5.964
283	9.320	5.894
284	6.110	5.657
285	5.500	5.517
286	6.010	6.218
287	5.060	5.876
288	5.440	5.409
289	5.580	6.011
290	6.210	5.789
291	6.000	5.731
292	8.440	7.077
293	13.240	6.548
294	5.680	4.489
295	5.540	5.297
296	7.100	6.058
297	5.110	5.506
298	6.000	5.256
299	6.410	5.320
300	7.900	6.157
301	5.190	4.919
302	5.980	5.689
303	9.090	6.636
304	5.480	5.276
305	5.340	5.852
306	5.180	6.557
307	5.250	5.474
308	5.880	5.611
309	5.280	5.443
310	5.600	6.215
311	4.540	6.079
312	4.860	4.695
313	15.240	7.808
314	5.200	4.911
315	5.600	5.442
316	6.070	5.271
317	6.660	5.593
318	9.360	6.436
319	5.210	6.136
320	4.810	5.902
321	4.820	5.407
322	4.730	5.596
323	5.330	5.211
324	5.420	6.098
325	5.660	5.954
326	5.300	6.267
327	5.930	5.027
328	5.500	5.623
329	5.550	6.355
330	5.410	6.907
331	5.130	6.263
332	5.690	5.486
333	5.540	6.341
334	5.340	5.845
335	5.950	5.710
336	5.670	6.049
337	5.600	5.921
338	5.440	5.509
339	7.740	6.675
340	5.490	5.932
341	5.860	5.529
342	6.100	4.731
343	5.560	5.204
344	5.620	5.123
345	5.390	5.456
346	4.910	5.658
347	5.420	5.471
348	5.530	5.049
349	5.330	6.287
350	5.230	4.697
351	4.860	4.851
352	5.340	5.514
353	6.730	5.585
354	4.910	5.478
355	4.980	5.634
356	6.810	5.074
357	5.010	5.255
358	5.840	4.930
359	5.920	5.690
360	5.260	5.486
361	5.820	6.001
362	4.990	4.826
363	5.350	5.599
364	5.560	6.591
365	5.340	6.212
366	6.250	5.842
367	5.250	5.050
368	6.950	6.273
369	5.490	5.757
370	9.030	5.671
371	5.290	5.886
372	6.990	5.986
373	4.950	4.818
374	4.570	6.601
375	4.550	4.466
376	5.680	5.835
377	4.990	5.587
378	5.910	6.436
379	5.280	5.734
380	5.020	5.153
381	5.740	5.683
382	4.940	5.080
383	7.780	5.800
384	5.640	5.359
385	5.040	5.655
386	6.500	6.341
387	5.880	6.396
388	5.510	5.434
389	4.980	4.949
390	5.370	6.044
391	5.850	5.934
392	5.950	5.988
393	5.200	4.737
394	5.390	6.168
395	6.570	5.831
396	7.050	5.969
397	5.800	5.458
398	5.880	6.240
399	5.130	4.924
400	5.240	5.581
401	4.930	5.936
402	5.440	5.241
403	8.790	6.662
404	4.840	4.829
405	5.360	6.079
406	5.100	5.704
407	4.830	6.083
408	10.680	6.756
409	5.020	5.827
410	5.840	6.660
411	3.850	5.552
412	5.050	5.428
413	6.070	7.079
414	5.400	5.363
415	5.710	5.911
416	4.950	5.036
417	5.130	5.815
418	5.400	5.644
419	5.600	6.126
420	6.240	5.690
421	6.370	5.517
422	6.560	6.337
423	4.980	5.451
424	5.000	5.123
425	5.130	5.536
426	5.350	5.191
427	5.150	5.418
428	4.580	5.027
429	5.990	5.667
430	4.980	4.462
431	5.670	5.297
432	5.150	6.085
433	5.280	6.092
434	5.490	6.790
435	7.880	6.326
436	12.790	6.462
437	5.020	5.337
438	5.060	6.272
439	4.750	5.689
440	5.640	5.506
441	4.930	5.758
442	5.980	6.023
443	5.120	5.781
444	4.920	5.872
445	5.380	5.829
446	9.680	6.185
447	5.430	5.990
448	5.130	5.475
449	5.540	5.522
450	5.360	5.700
451	13.840	6.176
452	5.820	5.576
453	4.980	5.993
454	7.440	5.680
455	5.740	6.160
456	6.490	5.898
457	4.580	5.498
458	5.930	5.715
459	10.420	6.314
460	14.900	6.303
461	5.150	5.850
462	4.910	5.483
463	5.100	5.296
464	7.330	5.875
465	4.930	5.731
466	5.810	6.087
467	4.960	5.290
468	5.260	5.670
469	5.510	5.477
470	5.220	5.567
471	5.300	5.106
472	4.720	5.468
473	5.340	5.425
474	5.230	5.600
475	5.480	5.525
476	5.660	5.530
477	5.230	5.142
478	5.140	5.486
479	4.700	4.632
480	6.200	5.626
481	5.110	5.662
482	6.840	4.900
483	5.280	4.890
484	5.150	5.321
485	6.030	5.518
486	4.470	5.311
487	5.460	5.159
488	4.540	5.339
489	5.090	5.339
490	4.380	5.262
491	5.160	4.848
492	4.060	5.340
493	5.560	5.416
494	5.610	5.289
495	5.240	4.495
496	4.670	4.927
497	5.570	5.174
498	4.410	5.041
499	5.150	4.853
500	4.710	5.271
501	5.410	5.072
502	5.030	5.344
503	11.790	5.565
504	4.930	5.354
505	5.800	5.455
506	5.160	5.355
507	5.480	4.713
508	4.850	5.209
509	5.310	5.066
510	5.680	5.359
511	6.020	5.505
512	5.050	5.789
513	5.170	5.547
514	4.990	5.704
515	5.030	5.491
516	4.970	4.952
517	6.040	6.071
518	5.020	5.266
519	5.350	5.106
520	5.050	5.608
521	5.190	5.092
522	6.030	5.426
523	5.720	5.276
524	7.020	5.496
525	5.080	5.100
526	5.580	5.385
527	4.760	5.418
528	5.120	4.735
529	4.530	5.081
530	4.890	5.199
531	4.870	5.008
532	5.160	5.319
533	4.600	5.010
534	7.720	5.464
535	5.300	5.396
536	5.200	4.763
537	4.850	4.957
538	5.170	4.411
539	5.010	4.837
540	5.400	5.273
541	4.620	5.042
542	6.370	6.101
543	4.910	5.001
544	13.100	5.977
545	7.500	5.751
546	4.500	5.100
547	5.830	5.983
548	5.040	5.265
549	5.950	5.995
550	5.690	5.618
551	5.540	6.325
552	5.320	5.559
553	5.470	5.372
554	5.090	5.511
555	5.180	5.920
556	5.870	5.528
557	5.540	5.705
558	5.600	5.137
559	4.810	4.609
560	5.680	5.867
561	5.820	6.694
562	5.320	5.895
563	5.590	5.716
564	12.890	6.644
565	7.550	6.158
566	4.730	4.860
567	9.340	5.545
568	5.710	5.796
569	11.300	6.652
570	4.850	4.921
571	6.300	6.294
572	5.170	5.545
573	4.710	4.624
574	5.750	5.386
575	4.890	4.637
576	5.970	5.772
577	5.080	5.135
578	4.860	5.594
579	9.560	6.198
580	4.790	5.100
581	5.240	4.786
582	5.680	5.870
583	4.990	4.925
584	6.450	5.740
585	5.130	5.520
586	4.820	5.483
587	6.230	5.385
588	5.610	6.042
589	8.800	6.408
590	4.820	6.236
591	4.840	6.048
592	4.820	6.254
593	5.940	6.347
594	5.670	4.897
595	5.540	5.666
596	5.250	5.836
597	5.840	5.020
598	6.610	5.601
599	5.740	4.891
600	5.690	6.046
601	14.020	6.834
602	5.830	7.352
603	4.870	5.071
604	5.570	6.541
605	5.840	5.625
606	5.290	6.300
607	5.810	6.262
608	7.970	6.337
609	5.870	5.917
610	5.180	5.080
611	5.270	5.994
612	5.690	5.119
613	5.620	5.802
614	5.710	4.752
615	5.790	6.023
616	4.930	4.783
617	5.320	6.006
618	4.810	4.800
619	6.840	5.914
620	5.280	4.830
621	5.310	6.748
622	5.060	6.071
623	5.360	5.871
624	4.660	5.313
625	5.360	5.767
626	5.390	6.464
627	5.160	5.874
628	7.090	7.291
629	6.430	5.590
630	4.690	5.332
631	4.660	6.485
632	5.030	4.942
633	4.860	5.241
634	6.350	5.723
635	5.730	5.271
636	5.930	5.922
637	5.760	5.249
638	5.590	5.966
639	6.140	5.567
640	8.000	6.110
641	6.080	5.917
642	5.220	5.410
643	6.340	6.766
644	4.710	5.100
645	4.720	5.563
646	5.340	6.388
647	6.210	6.325
648	9.050	6.472
649	5.340	6.242
650	4.690	4.491
651	5.210	5.967
652	4.730	5.746
653	5.170	5.039
654	5.850	5.387
655	4.930	5.750
656	4.960	6.271
657	5.090	5.042
658	5.840	5.036
659	4.620	5.338
660	5.380	5.637
661	5.560	6.047
662	5.430	4.816
663	4.750	5.726
664	5.290	4.914
665	4.940	5.400
666	5.450	5.581
667	5.610	6.186
668	6.960	6.170
669	5.800	5.912
670	5.310	5.447
671	4.990	4.681
672	6.080	5.915
673	4.440	5.116
674	5.130	5.931
675	5.420	5.870
676	5.410	5.936
677	4.910	4.939
678	4.950	6.223
679	5.090	6.063
680	5.730	5.146
681	4.920	5.198
682	4.790	5.005
683	5.780	4.745
684	20.460	6.469
685	11.230	5.978
686	4.910	5.790
687	5.210	5.936
688	5.560	5.686
689	5.080	5.062
690	5.660	4.694
691	6.020	5.599
692	4.860	5.268
693	5.000	6.157
694	5.900	5.094
695	4.780	4.311
696	5.440	8.176
697	5.380	6.450
698	5.710	5.074
699	5.800	6.132
700	5.270	4.563
701	5.500	5.168
702	4.790	5.161
703	4.790	5.551
704	5.340	6.002
705	4.750	5.373
706	4.950	4.701
707	5.610	4.688
708	4.900	4.560
709	5.950	5.151
710	5.330	5.376
711	5.150	5.259
712	4.510	4.608
713	6.160	5.834
714	6.100	6.066
715	5.040	6.591
716	4.150	5.355
717	5.080	6.017
718	5.350	4.274
719	4.850	6.084
720	4.820	5.719
721	5.870	5.971
722	6.140	6.944
723	4.910	5.204
724	7.860	6.119
725	5.450	4.835
726	6.230	6.266
727	4.430	6.132
728	5.170	4.900
729	4.860	5.355
730	5.010	5.747
731	4.990	5.529
732	6.720	5.837
733	5.590	6.344
734	4.870	5.862
735	4.900	5.712
736	5.120	6.581
737	5.360	6.078
738	4.910	5.789
739	4.770	5.579
740	6.190	6.121
741	5.180	4.973
742	5.140	4.913
743	4.600	5.121
744	4.610	6.501
745	7.700	6.881
746	5.080	5.486
747	8.670	5.757
748	6.300	5.886
749	5.630	5.579
750	5.790	5.483
751	5.640	5.080
752	4.570	5.182
753	5.580	5.245
754	6.230	5.040
755	5.450	4.781
756	5.620	4.433
757	5.280	4.820
758	4.660	5.616
759	4.060	5.312
760	5.000	5.014
761	4.790	4.911
762	5.530	6.252
763	7.480	6.450
764	5.080	6.105
765	5.310	5.557
766	5.180	4.642
767	4.960	5.627
768	5.260	5.683
769	6.920	5.738
770	5.380	5.824
771	4.590	5.911
772	4.990	5.450
773	7.600	6.536
774	5.340	5.926
775	4.900	5.085
776	5.280	5.654
777	7.800	6.169
778	8.620	5.696
779	4.770	4.687
780	5.310	6.453
781	4.910	5.945
782	8.420	6.104
783	4.760	5.116
784	4.520	5.558
785	4.640	5.536
786	4.990	6.359
787	5.140	5.075
788	5.870	6.866
789	5.650	5.923
790	5.130	5.602
791	5.790	6.189
792	4.740	5.877
793	6.160	5.919
794	5.210	6.600
795	5.250	6.179
796	5.060	5.659
797	5.840	6.040
798	4.960	5.428
799	5.400	6.513
800	5.570	5.801
801	5.180	5.083
802	5.280	4.901
803	4.800	5.626
804	5.230	4.665
805	5.140	6.152
806	5.730	5.685
807	5.220	5.212
808	5.320	6.259
809	5.170	5.689
810	4.850	5.607
811	5.380	5.857
812	5.010	5.951
813	6.290	4.860
814	5.840	5.487
815	4.800	6.154
816	8.300	5.452
817	5.140	6.116
818	6.220	6.139
819	4.830	5.945
820	4.830	5.457
821	6.100	6.178
822	5.980	8.084
823	5.760	6.643
824	5.000	5.181
825	5.660	6.156
826	5.370	5.154
827	5.890	6.192
828	5.370	5.534
829	5.930	6.000
830	5.730	5.718
831	5.360	5.222
832	7.100	6.144
833	5.300	5.211
834	5.140	5.621
835	5.610	5.343
836	4.740	5.224
837	4.750	5.308
838	5.130	5.489
839	11.610	6.046
840	5.510	6.378
841	5.620	6.076
842	5.070	5.113
843	4.770	4.779
844	8.570	6.766
845	7.090	5.715
846	7.190	8.221
847	4.650	4.965
848	5.570	5.654
849	5.910	5.473
850	6.780	5.595
851	5.270	4.944
852	4.710	4.776
853	5.880	5.721
854	5.050	5.397
855	5.620	5.840
856	4.990	5.919
857	5.180	5.445
858	5.280	6.529
859	5.950	6.079
860	5.560	5.558
861	5.560	5.768
862	5.710	5.586
863	5.670	5.457
864	4.740	5.275
865	4.670	5.219
866	5.310	5.740
867	5.430	5.237
868	5.820	5.286
869	4.460	5.237
870	5.990	5.297
871	7.350	6.322
872	4.550	5.076
873	5.490	5.800
874	4.900	5.648
875	5.420	5.595
876	5.350	5.753
877	5.100	5.271
878	5.530	4.713
879	5.630	5.421
880	5.100	5.424
881	6.360	5.965
882	6.510	6.232
883	5.260	6.126
884	4.490	5.255
885	7.630	5.728
886	5.040	5.128
887	5.820	5.628
888	5.500	5.418
889	5.410	4.683
890	5.370	6.133
891	5.480	6.109
892	9.640	5.821
893	5.300	5.567
894	5.470	5.060
895	5.390	5.021
896	5.460	6.041
897	5.830	5.974
898	5.160	6.623
899	6.140	4.943
900	5.650	5.671
901	5.480	5.348
902	5.310	5.708
903	4.720	6.008
904	6.010	6.296
905	5.110	6.434
906	5.620	5.300
907	4.750	4.719
908	5.410	5.353
909	5.520	5.649
910	4.780	5.778
911	5.460	6.037
912	5.130	5.526
913	5.900	5.955
914	12.440	7.154
915	5.690	5.909
916	6.910	6.071
917	5.380	5.906
918	4.910	5.592
919	5.290	5.238
920	5.050	5.044
921	7.230	6.479
922	4.720	5.266
923	5.060	6.415
924	5.930	6.091
925	5.620	4.640
926	5.400	5.592
927	5.190	6.257
928	6.290	8.757
929	6.200	5.802
930	8.870	6.884
931	5.620	6.010
932	5.310	6.166
933	6.450	6.149
934	6.340	5.757
935	5.570	6.001
936	5.710	4.764
937	5.430	6.000
938	17.530	7.987
939	5.210	5.154
940	5.370	6.519
941	5.460	5.703
942	5.540	5.081
943	5.660	4.988
944	5.440	5.431
945	5.750	6.118
946	5.180	4.786
947	5.190	4.966
948	5.250	4.868
949	5.590	4.692
950	5.320	5.865
951	7.670	8.725
952	6.020	6.163
953	5.200	7.502
954	5.790	6.062
955	4.880	5.478
956	4.520	5.286
957	4.880	6.249
958	4.710	6.225
959	6.980	7.118
960	5.360	5.931
961	5.410	6.311
962	5.590	5.519
963	5.410	5.318
964	4.860	5.557
965	5.330	5.127
966	5.540	4.638
967	4.910	4.869
968	5.510	6.606
969	4.760	5.588
970	6.100	6.311
971	5.260	6.622
972	5.030	6.168
973	11.110	6.982
974	5.320	5.900
975	5.260	5.849
976	5.500	5.974
977	4.900	5.536
978	5.020	5.614
979	5.130	4.757
980	6.070	6.260
981	5.100	4.783
982	5.490	5.939
983	6.010	5.701
984	4.860	6.571
985	7.250	6.107
986	5.050	5.410
987	11.390	5.836
988	6.250	5.928
989	5.020	6.099
990	5.120	5.956
991	5.020	5.181
992	5.270	5.150
993	5.940	6.664
994	6.580	6.530
995	5.400	5.600
996	5.500	6.269
997	5.330	6.944
998	5.540	5.014
999	6.360	5.471

	sex	age	AST	ALT	ALP	GGT	TP	Alb	GLB	A/G	TG	TCHO	HDL-C	LDL-C	Urea	Cre	UA	HBsAg	Second liver antibody	HBeAg	HBeAb	HBcAb	WBC	RBC	HGB	PCV	MCV	MCH	MCHC	RDW	PLT	MPV	PDW	PCT	NEUT%	LY%	MONO%	EOS%	BAS%
0	0	41	24.96	23.10	99.59	20.23	76.88	49.60	27.28	1.82	1.31	4.43	1.37	2.65	5.87	77.25	349.39	NaN	NaN	NaN	NaN	NaN	5.34	5.21	166.1	0.479	91.9	31.9	347.0	12.8	166.0	9.9	17.4	0.164	54.1	34.2	6.5	4.7	0.6
1	0	41	24.57	36.25	67.21	79.00	79.43	47.76	31.67	1.51	2.81	4.06	0.93	2.63	5.26	87.12	486.78	NaN	NaN	NaN	NaN	NaN	7.65	5.21	156.0	0.456	87.5	29.9	342.0	13.4	277.0	9.2	10.3	0.260	52.0	36.7	5.8	4.7	0.8
2	0	46	20.82	15.23	63.69	38.17	86.23	48.00	38.23	1.26	0.99	4.13	1.64	2.01	4.77	78.19	452.07	0.010	0.020	0.010	1.370	1.070	4.60	4.76	148.8	0.438	91.9	31.3	340.0	13.0	241.0	8.3	16.6	0.199	48.1	40.3	7.7	3.2	0.8
3	1	22	14.99	10.59	74.08	20.22	70.98	44.02	26.96	1.63	1.06	6.89	1.43	5.04	4.23	61.46	368.85	NaN	NaN	NaN	NaN	NaN	9.36	4.29	137.0	0.403	93.9	31.9	340.0	12.6	252.0	10.3	10.8	0.260	41.7	46.5	6.7	4.6	0.5
4	1	48	20.07	14.78	75.79	22.72	78.05	41.83	36.22	1.15	0.97	5.37	1.27	3.65	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	5.07	5.15	106.0	0.354	68.7	20.6	299.0	16.6	316.0	11.1	14.0	0.350	56.6	33.1	9.1	0.6	0.6
5	1	74	23.72	22.59	81.23	23.35	76.46	45.85	30.61	1.50	2.45	6.65	1.81	4.28	6.11	66.66	383.87	0.050	0.550	0.010	1.220	1.570	7.00	4.64	141.3	0.413	88.8	30.4	343.0	13.5	249.0	8.5	17.0	0.211	42.9	47.0	7.1	2.1	1.0
6	0	31	24.97	25.53	109.03	65.42	80.82	46.40	34.42	1.35	1.51	5.60	1.33	3.82	6.14	95.98	446.10	NaN	NaN	NaN	NaN	NaN	6.81	6.03	172.0	0.504	83.6	28.5	341.0	12.0	246.0	10.8	13.3	0.270	52.9	32.0	11.3	3.1	0.7
7	0	55	37.32	40.03	88.49	25.15	74.17	41.63	32.54	1.28	1.32	5.92	1.28	4.20	6.31	92.16	344.73	NaN	NaN	NaN	NaN	NaN	5.78	4.90	158.0	0.448	91.4	32.2	353.0	12.5	282.0	10.5	13.0	0.300	52.8	36.9	6.6	2.8	0.9
8	0	39	21.70	39.17	102.91	67.09	76.12	49.31	26.81	1.84	2.07	5.50	1.51	3.60	4.98	78.57	353.31	0.060	0.100	0.030	2.230	2.170	6.96	4.94	163.0	0.482	97.6	33.0	338.0	11.8	275.0	9.8	11.4	0.270	53.1	35.9	7.3	3.3	0.4
9	1	35	18.89	15.79	78.21	25.75	75.94	46.64	29.30	1.59	1.25	3.98	1.55	2.02	6.61	86.88	266.52	0.010	0.020	0.020	2.450	2.350	8.91	4.81	144.0	0.437	90.9	29.9	330.0	11.8	247.0	10.7	12.6	0.270	65.6	27.3	5.4	1.0	0.7
10	0	47	27.33	35.76	80.26	84.97	75.84	43.71	32.13	1.36	1.96	4.32	0.90	2.84	5.60	66.49	333.60	NaN	NaN	NaN	NaN	NaN	5.44	4.97	160.0	0.452	90.9	32.2	354.0	12.4	178.0	10.8	13.5	0.190	54.0	35.7	8.1	1.3	0.9
11	0	50	22.89	28.46	77.54	48.16	73.80	48.32	25.48	1.90	3.32	4.47	1.14	2.65	4.69	74.53	493.07	NaN	NaN	NaN	NaN	NaN	6.80	4.86	151.0	0.453	93.2	31.1	333.0	11.9	259.0	10.3	12.0	0.270	57.8	33.1	7.5	1.2	0.4
12	1	48	30.11	34.70	92.67	16.39	77.86	43.05	34.81	1.24	1.21	3.69	1.43	1.94	4.39	67.54	264.32	NaN	NaN	NaN	NaN	NaN	5.26	4.89	137.0	0.407	83.2	28.0	337.0	12.0	223.0	12.4	17.2	0.280	38.8	48.5	7.2	4.2	1.3
13	1	36	16.82	11.80	67.79	13.16	76.84	47.56	29.28	1.62	0.27	2.85	1.93	0.56	3.47	73.26	345.48	NaN	NaN	NaN	NaN	NaN	6.67	4.58	141.0	0.415	90.6	30.8	340.0	11.9	330.0	9.2	9.9	0.300	72.5	20.5	6.1	0.6	0.3
14	1	37	24.58	24.64	96.43	27.79	92.31	46.98	45.33	1.04	1.22	5.20	1.21	3.55	3.46	65.99	383.20	NaN	NaN	NaN	NaN	NaN	8.87	4.69	146.0	0.428	91.3	31.1	341.0	12.6	344.0	11.9	15.5	0.410	57.5	33.4	6.8	1.5	0.8
15	1	43	20.07	14.81	72.11	33.98	74.43	44.49	29.94	1.49	1.52	5.51	1.58	3.52	5.67	62.26	300.92	NaN	NaN	NaN	NaN	NaN	10.61	4.48	136.0	0.409	91.3	30.4	333.0	12.7	424.0	10.7	12.7	0.450	64.9	28.5	5.0	1.1	0.5
16	0	77	18.49	17.29	104.91	41.33	76.02	42.39	33.63	1.26	4.59	4.05	1.02	2.49	10.68	134.63	437.37	NaN	NaN	NaN	NaN	NaN	12.84	4.95	158.0	0.442	89.3	31.9	357.0	12.0	303.0	9.9	11.5	0.300	73.0	17.5	7.2	1.9	0.4
17	0	42	24.71	30.26	95.70	37.97	81.87	43.85	38.02	1.15	3.87	5.83	1.11	4.07	6.77	90.66	485.17	NaN	NaN	NaN	NaN	NaN	8.13	6.39	121.0	0.392	61.3	18.9	309.0	18.6	263.0	11.4	16.1	0.300	55.0	35.8	8.0	1.0	0.2
18	0	39	31.44	43.97	92.58	35.84	79.74	46.49	33.25	1.40	1.33	4.55	1.11	3.40	4.75	107.33	565.31	NaN	NaN	NaN	NaN	NaN	8.88	5.34	155.0	0.466	87.3	29.0	333.0	13.2	274.0	11.2	13.4	0.310	61.5	28.2	6.0	3.5	0.8
19	0	38	23.58	31.19	102.16	44.01	81.50	51.16	30.34	1.69	2.63	5.93	1.20	4.29	3.07	94.16	468.95	NaN	NaN	NaN	NaN	NaN	6.74	5.70	171.0	0.498	87.4	30.0	343.0	13.9	193.0	11.2	13.6	0.220	54.4	36.8	7.1	1.3	0.4
20	0	30	33.25	67.19	82.22	59.91	82.80	49.32	33.48	1.47	3.44	5.50	1.18	4.02	3.79	103.15	493.38	NaN	NaN	NaN	NaN	NaN	6.44	6.12	181.0	0.540	88.2	29.6	335.0	12.1	256.0	11.6	16.2	0.300	58.4	33.7	5.3	2.0	0.6
21	0	51	26.53	20.44	67.03	25.84	75.70	48.68	27.02	1.80	1.87	4.48	1.36	2.81	4.25	98.69	398.20	NaN	NaN	NaN	NaN	NaN	6.46	5.25	175.2	0.509	97.0	33.4	344.0	12.9	166.0	9.9	17.2	0.164	58.2	33.9	5.2	2.4	0.3
22	1	38	24.13	34.02	77.32	18.78	76.12	44.87	31.25	1.44	1.42	4.78	1.73	2.71	3.56	65.55	236.15	NaN	NaN	NaN	NaN	NaN	5.98	3.31	81.0	0.264	79.8	24.5	307.0	12.8	571.0	10.1	10.1	0.580	55.9	27.6	11.5	4.3	0.7
23	1	33	15.77	11.43	95.86	20.45	83.11	47.37	35.74	1.33	0.94	3.49	1.22	1.97	5.93	65.04	254.75	NaN	NaN	NaN	NaN	NaN	5.48	4.68	140.0	0.412	88.0	29.9	340.0	12.1	196.0	11.5	14.2	0.230	45.8	47.4	5.3	1.1	0.4
24	1	28	20.24	12.55	61.90	7.99	76.97	47.89	29.08	1.65	0.60	4.88	1.92	2.45	4.27	71.37	241.98	NaN	NaN	NaN	NaN	NaN	4.90	4.86	147.0	0.440	90.5	30.2	334.0	12.7	206.0	10.4	11.5	0.210	49.0	44.1	5.5	0.8	0.6
25	1	34	20.89	16.16	102.40	14.69	77.07	43.72	33.35	1.31	1.40	5.93	1.47	4.37	4.36	68.11	263.35	NaN	NaN	NaN	NaN	NaN	7.78	4.90	139.0	0.418	85.3	28.4	333.0	13.9	293.0	11.8	15.4	0.350	55.4	39.2	4.5	0.3	0.6
26	0	34	32.03	52.74	102.73	34.53	79.11	50.40	28.71	1.76	1.18	5.93	1.68	4.13	6.20	80.23	446.16	NaN	NaN	NaN	NaN	NaN	6.33	5.17	153.0	0.458	88.6	29.6	334.0	12.0	264.0	9.4	10.0	0.250	56.1	35.4	6.8	1.1	0.6
27	0	38	34.62	54.43	119.68	51.14	78.47	49.26	29.21	1.69	2.46	5.37	1.12	3.92	4.00	88.32	336.44	NaN	NaN	NaN	NaN	NaN	7.26	5.58	172.0	0.506	90.7	30.8	340.0	12.2	223.0	11.1	15.1	0.250	55.1	33.1	9.1	1.9	0.8
28	0	60	28.36	32.30	64.43	30.76	71.38	43.21	28.17	1.53	0.92	4.59	1.53	2.69	8.25	78.72	322.39	NaN	NaN	NaN	NaN	NaN	4.83	5.04	156.0	0.468	92.9	31.0	333.0	12.3	201.0	10.5	11.4	0.210	54.2	35.8	7.5	1.7	0.8
29	0	55	29.60	34.07	99.96	41.95	82.33	45.49	36.84	1.23	1.62	5.28	1.25	3.58	5.33	88.48	389.35	NaN	NaN	NaN	NaN	NaN	5.85	5.40	165.0	0.493	91.3	30.6	335.0	13.2	260.0	11.1	13.5	0.290	55.7	30.1	10.4	2.6	1.2
30	0	56	22.68	16.93	102.98	30.43	73.75	47.42	26.33	1.80	0.69	5.41	2.07	2.85	3.66	68.16	245.80	NaN	NaN	NaN	NaN	NaN	7.78	4.71	150.0	0.449	95.3	31.8	334.0	12.6	308.0	10.4	12.1	0.320	50.4	38.0	9.4	1.8	0.4
31	0	59	39.70	28.17	118.78	22.64	76.72	43.77	32.95	1.33	0.78	5.43	1.73	3.43	5.99	96.64	278.50	NaN	NaN	NaN	NaN	NaN	6.20	5.60	166.0	0.482	86.1	29.6	344.0	13.1	187.0	10.2	12.1	0.190	50.3	42.1	6.8	0.5	0.3
32	0	58	20.84	23.66	83.13	150.09	71.55	44.96	26.59	1.69	4.85	6.78	1.77	4.50	7.52	96.31	323.78	NaN	NaN	NaN	NaN	NaN	9.89	4.60	152.0	0.432	93.9	33.0	352.0	12.4	273.0	9.8	10.6	0.270	39.1	53.0	5.9	1.3	0.7
33	0	32	30.23	39.73	67.52	27.26	79.21	46.96	32.25	1.46	2.10	6.06	1.35	4.10	4.35	73.93	389.17	NaN	NaN	NaN	NaN	NaN	4.96	5.41	161.0	0.473	87.4	29.8	340.0	12.1	253.0	10.8	13.5	0.270	56.8	33.1	8.1	1.4	0.6
34	0	35	20.93	37.67	64.63	46.95	75.52	46.23	29.29	1.58	3.51	4.77	0.88	3.29	4.09	100.13	378.25	NaN	NaN	NaN	NaN	NaN	6.71	5.08	158.0	0.463	91.1	31.1	341.0	12.3	210.0	10.4	12.6	0.220	34.1	52.5	7.9	4.9	0.6
35	1	38	18.81	12.55	52.55	16.03	81.27	48.17	33.10	1.46	1.02	5.30	1.46	3.48	8.48	77.19	366.38	NaN	NaN	NaN	NaN	NaN	6.93	5.04	145.0	0.430	85.3	28.8	337.0	13.0	272.0	10.1	11.7	0.270	55.4	35.1	6.9	2.2	0.4
36	1	47	26.58	16.03	71.79	30.68	82.17	42.87	39.30	1.09	1.43	5.76	1.24	4.02	3.48	63.55	243.69	NaN	NaN	NaN	NaN	NaN	7.36	4.53	134.9	0.396	87.3	29.8	341.0	13.3	335.0	8.5	15.7	0.283	51.6	35.0	8.0	4.6	0.8
37	1	36	17.99	10.11	55.82	26.14	75.47	43.18	32.29	1.34	1.53	5.70	1.27	3.92	6.48	69.54	311.17	NaN	NaN	NaN	NaN	NaN	7.48	4.02	129.0	0.375	93.3	32.1	344.0	12.3	274.0	10.0	11.1	0.270	57.6	35.0	6.0	0.9	0.5
38	1	45	20.58	11.25	60.21	11.62	75.65	43.98	31.67	1.39	0.94	4.63	1.65	2.75	5.38	73.60	227.93	NaN	NaN	NaN	NaN	NaN	6.12	4.51	135.6	0.405	89.8	30.0	335.0	12.7	213.0	8.7	16.5	0.185	50.0	40.9	7.9	0.6	0.5
39	1	53	20.47	10.76	132.27	21.11	76.45	46.28	30.17	1.53	1.47	6.65	1.66	4.37	3.06	65.44	225.15	NaN	NaN	NaN	NaN	NaN	5.42	4.46	134.0	0.408	91.5	30.0	328.0	13.1	402.0	9.7	10.5	0.390	47.8	43.2	7.0	1.3	0.7

	GLU
0	6.06
1	5.39
2	5.59
3	4.30
4	5.42
5	5.97
6	5.11
7	5.94
8	5.66
9	5.48
10	4.44
11	5.48
12	5.32
13	5.08
14	4.63
15	4.92
16	7.08
17	4.66
18	6.58
19	4.29
20	4.55
21	5.16
22	5.13
23	4.37
24	5.17
25	4.64
26	5.82
27	6.29
28	5.10
29	5.41
30	5.48
31	6.44
32	5.53
33	5.31
34	5.52
35	4.70
36	5.13
37	6.06
38	5.41
39	5.82

	sex	age	AST	ALT	ALP	GGT	TP	Alb	GLB	A/G	TG	TCHO	HDL-C	LDL-C	Urea	Cre	UA	HBsAg	Second liver antibody	HBeAg	HBeAb	HBcAb	WBC	RBC	HGB	PCV	MCV	MCH	MCHC	RDW	PLT	MPV	PDW	PCT	NEUT%	LY%	MONO%	EOS%	BAS%
0	0	41	24.960000	23.100000	99.590000	20.230000	76.880000	49.600000	27.280000	1.820000	1.310000	4.430000	1.370000	2.650000	5.870000	77.250000	349.390000	0.412369	6.624946	0.079442	1.805167	2.028279	5.340000	5.210	166.100000	0.4790	91.90	31.90	347.000000	12.800000	166.000000	9.900000	17.400000	0.1640	54.100000	34.200000	6.500000	4.700000	0.600000
1	0	41	24.570000	36.250000	67.210000	79.000000	79.430000	47.760000	31.670000	1.510000	2.810000	4.060000	0.930000	2.630000	5.260000	87.120000	486.780000	0.412369	6.624946	0.079442	1.805167	2.028279	7.650000	5.210	156.000000	0.4560	87.50	29.90	342.000000	13.400000	277.000000	9.200000	10.300000	0.2600	52.000000	36.700000	5.800000	4.700000	0.800000
2	0	46	20.820000	15.230000	63.690000	38.170000	86.230000	48.000000	38.230000	1.260000	0.990000	4.130000	1.640000	2.010000	4.770000	78.190000	452.070000	0.010000	0.020000	0.010000	1.370000	1.070000	4.600000	4.760	148.800000	0.4380	91.90	31.30	340.000000	13.000000	241.000000	8.300000	16.600000	0.1990	48.100000	40.300000	7.700000	3.200000	0.800000
3	1	22	14.990000	10.590000	74.080000	20.220000	70.980000	44.020000	26.960000	1.630000	1.060000	6.890000	1.430000	5.040000	4.230000	61.460000	368.850000	0.023333	0.196667	0.010000	1.320000	1.236667	9.360000	4.290	137.000000	0.4030	93.90	31.90	340.000000	12.600000	252.000000	10.300000	10.800000	0.2600	41.700000	46.500000	6.700000	4.600000	0.500000
4	1	48	20.070000	14.780000	75.790000	22.720000	78.050000	41.830000	36.220000	1.150000	0.970000	5.370000	1.270000	3.650000	5.170000	64.060000	376.360000	0.036667	0.373333	0.010000	1.270000	1.403333	5.070000	5.150	106.000000	0.3540	68.70	20.60	299.000000	16.600000	316.000000	11.100000	14.000000	0.3500	56.600000	33.100000	9.100000	0.600000	0.600000
5	1	74	23.720000	22.590000	81.230000	23.350000	76.460000	45.850000	30.610000	1.500000	2.450000	6.650000	1.810000	4.280000	6.110000	66.660000	383.870000	0.050000	0.550000	0.010000	1.220000	1.570000	7.000000	4.640	141.300000	0.4130	88.80	30.40	343.000000	13.500000	249.000000	8.500000	17.000000	0.2110	42.900000	47.000000	7.100000	2.100000	1.000000
6	0	31	24.970000	25.530000	109.030000	65.420000	80.820000	46.400000	34.420000	1.350000	1.510000	5.600000	1.330000	3.820000	6.140000	95.980000	446.100000	0.053333	0.400000	0.016667	1.556667	1.770000	6.810000	6.030	172.000000	0.5040	83.60	28.50	341.000000	12.000000	246.000000	10.800000	13.300000	0.2700	52.900000	32.000000	11.300000	3.100000	0.700000
7	0	55	37.320000	40.030000	88.490000	25.150000	74.170000	41.630000	32.540000	1.280000	1.320000	5.920000	1.280000	4.200000	6.310000	92.160000	344.730000	0.056667	0.250000	0.023333	1.893333	1.970000	5.780000	4.900	158.000000	0.4480	91.40	32.20	353.000000	12.500000	282.000000	10.500000	13.000000	0.3000	52.800000	36.900000	6.600000	2.800000	0.900000
8	0	39	21.700000	39.170000	102.910000	67.090000	76.120000	49.310000	26.810000	1.840000	2.070000	5.500000	1.510000	3.600000	4.980000	78.570000	353.310000	0.060000	0.100000	0.030000	2.230000	2.170000	6.960000	4.940	163.000000	0.4820	97.60	33.00	338.000000	11.800000	275.000000	9.800000	11.400000	0.2700	53.100000	35.900000	7.300000	3.300000	0.400000
9	1	35	18.890000	15.790000	78.210000	25.750000	75.940000	46.640000	29.300000	1.590000	1.250000	3.980000	1.550000	2.020000	6.610000	86.880000	266.520000	0.010000	0.020000	0.020000	2.450000	2.350000	8.910000	4.810	144.000000	0.4370	90.90	29.90	330.000000	11.800000	247.000000	10.700000	12.600000	0.2700	65.600000	27.300000	5.400000	1.000000	0.700000
10	0	47	27.330000	35.760000	80.260000	84.970000	75.840000	43.710000	32.130000	1.360000	1.960000	4.320000	0.900000	2.840000	5.600000	66.490000	333.600000	0.010321	0.020641	0.020256	2.443333	2.346346	5.440000	4.970	160.000000	0.4520	90.90	32.20	354.000000	12.400000	178.000000	10.800000	13.500000	0.1900	54.000000	35.700000	8.100000	1.300000	0.900000
11	0	50	22.890000	28.460000	77.540000	48.160000	73.800000	48.320000	25.480000	1.900000	3.320000	4.470000	1.140000	2.650000	4.690000	74.530000	493.070000	0.010641	0.021282	0.020513	2.436667	2.342692	6.800000	4.860	151.000000	0.4530	93.20	31.10	333.000000	11.900000	259.000000	10.300000	12.000000	0.2700	57.800000	33.100000	7.500000	1.200000	0.400000
12	1	48	30.110000	34.700000	92.670000	16.390000	77.860000	43.050000	34.810000	1.240000	1.210000	3.690000	1.430000	1.940000	4.390000	67.540000	264.320000	0.010962	0.021923	0.020769	2.430000	2.339038	5.260000	4.890	137.000000	0.4070	83.20	28.00	337.000000	12.000000	223.000000	12.400000	17.200000	0.2800	38.800000	48.500000	7.200000	4.200000	1.300000
13	1	36	16.820000	11.800000	67.790000	13.160000	76.840000	47.560000	29.280000	1.620000	0.270000	2.850000	1.930000	0.560000	3.470000	73.260000	345.480000	0.011282	0.022564	0.021026	2.423333	2.335385	6.670000	4.580	141.000000	0.4150	90.60	30.80	340.000000	11.900000	330.000000	9.200000	9.900000	0.3000	72.500000	20.500000	6.100000	0.600000	0.300000
14	1	37	24.580000	24.640000	96.430000	27.790000	92.310000	46.980000	45.330000	1.040000	1.220000	5.200000	1.210000	3.550000	3.460000	65.990000	383.200000	0.011603	0.023205	0.021282	2.416667	2.331731	8.870000	4.690	146.000000	0.4280	91.30	31.10	341.000000	12.600000	344.000000	11.900000	15.500000	0.4100	57.500000	33.400000	6.800000	1.500000	0.800000
15	1	43	20.070000	14.810000	72.110000	33.980000	74.430000	44.490000	29.940000	1.490000	1.520000	5.510000	1.580000	3.520000	5.670000	62.260000	300.920000	0.011923	0.023846	0.021538	2.410000	2.328077	10.610000	4.480	136.000000	0.4090	91.30	30.40	333.000000	12.700000	424.000000	10.700000	12.700000	0.4500	64.900000	28.500000	5.000000	1.100000	0.500000
16	0	77	18.490000	17.290000	104.910000	41.330000	76.020000	42.390000	33.630000	1.260000	4.590000	4.050000	1.020000	2.490000	10.680000	134.630000	437.370000	0.012244	0.024487	0.021795	2.403333	2.324423	12.840000	4.950	158.000000	0.4420	89.30	31.90	357.000000	12.000000	303.000000	9.900000	11.500000	0.3000	73.000000	17.500000	7.200000	1.900000	0.400000
17	0	42	24.710000	30.260000	95.700000	37.970000	81.870000	43.850000	38.020000	1.150000	3.870000	5.830000	1.110000	4.070000	6.770000	90.660000	485.170000	0.012564	0.025128	0.022051	2.396667	2.320769	8.130000	6.390	121.000000	0.3920	61.30	18.90	309.000000	18.600000	263.000000	11.400000	16.100000	0.3000	55.000000	35.800000	8.000000	1.000000	0.200000
18	0	39	31.440000	43.970000	92.580000	35.840000	79.740000	46.490000	33.250000	1.400000	1.330000	4.550000	1.110000	3.400000	4.750000	107.330000	565.310000	0.012885	0.025769	0.022308	2.390000	2.317115	8.880000	5.340	155.000000	0.4660	87.30	29.00	333.000000	13.200000	274.000000	11.200000	13.400000	0.3100	61.500000	28.200000	6.000000	3.500000	0.800000
19	0	38	23.580000	31.190000	102.160000	44.010000	81.500000	51.160000	30.340000	1.690000	2.630000	5.930000	1.200000	4.290000	3.070000	94.160000	468.950000	0.013205	0.026410	0.022564	2.383333	2.313462	6.740000	5.700	171.000000	0.4980	87.40	30.00	343.000000	13.900000	193.000000	11.200000	13.600000	0.2200	54.400000	36.800000	7.100000	1.300000	0.400000
20	0	30	33.250000	67.190000	82.220000	59.910000	82.800000	49.320000	33.480000	1.470000	3.440000	5.500000	1.180000	4.020000	3.790000	103.150000	493.380000	0.013526	0.027051	0.022821	2.376667	2.309808	6.440000	6.120	181.000000	0.5400	88.20	29.60	335.000000	12.100000	256.000000	11.600000	16.200000	0.3000	58.400000	33.700000	5.300000	2.000000	0.600000
21	0	51	26.530000	20.440000	67.030000	25.840000	75.700000	48.680000	27.020000	1.800000	1.870000	4.480000	1.360000	2.810000	4.250000	98.690000	398.200000	0.013846	0.027692	0.023077	2.370000	2.306154	6.460000	5.250	175.200000	0.5090	97.00	33.40	344.000000	12.900000	166.000000	9.900000	17.200000	0.1640	58.200000	33.900000	5.200000	2.400000	0.300000
22	1	38	24.130000	34.020000	77.320000	18.780000	76.120000	44.870000	31.250000	1.440000	1.420000	4.780000	1.730000	2.710000	3.560000	65.550000	236.150000	0.014167	0.028333	0.023333	2.363333	2.302500	5.980000	3.310	81.000000	0.2640	79.80	24.50	307.000000	12.800000	571.000000	10.100000	10.100000	0.5800	55.900000	27.600000	11.500000	4.300000	0.700000
23	1	33	15.770000	11.430000	95.860000	20.450000	83.110000	47.370000	35.740000	1.330000	0.940000	3.490000	1.220000	1.970000	5.930000	65.040000	254.750000	0.014487	0.028974	0.023590	2.356667	2.298846	5.480000	4.680	140.000000	0.4120	88.00	29.90	340.000000	12.100000	196.000000	11.500000	14.200000	0.2300	45.800000	47.400000	5.300000	1.100000	0.400000
24	1	28	20.240000	12.550000	61.900000	7.990000	76.970000	47.890000	29.080000	1.650000	0.600000	4.880000	1.920000	2.450000	4.270000	71.370000	241.980000	0.014808	0.029615	0.023846	2.350000	2.295192	4.900000	4.860	147.000000	0.4400	90.50	30.20	334.000000	12.700000	206.000000	10.400000	11.500000	0.2100	49.000000	44.100000	5.500000	0.800000	0.600000
25	1	34	20.890000	16.160000	102.400000	14.690000	77.070000	43.720000	33.350000	1.310000	1.400000	5.930000	1.470000	4.370000	4.360000	68.110000	263.350000	0.015128	0.030256	0.024103	2.343333	2.291538	7.780000	4.900	139.000000	0.4180	85.30	28.40	333.000000	13.900000	293.000000	11.800000	15.400000	0.3500	55.400000	39.200000	4.500000	0.300000	0.600000
26	0	34	32.030000	52.740000	102.730000	34.530000	79.110000	50.400000	28.710000	1.760000	1.180000	5.930000	1.680000	4.130000	6.200000	80.230000	446.160000	0.015449	0.030897	0.024359	2.336667	2.287885	6.330000	5.170	153.000000	0.4580	88.60	29.60	334.000000	12.000000	264.000000	9.400000	10.000000	0.2500	56.100000	35.400000	6.800000	1.100000	0.600000
27	0	38	34.620000	54.430000	119.680000	51.140000	78.470000	49.260000	29.210000	1.690000	2.460000	5.370000	1.120000	3.920000	4.000000	88.320000	336.440000	0.015769	0.031538	0.024615	2.330000	2.284231	7.260000	5.580	172.000000	0.5060	90.70	30.80	340.000000	12.200000	223.000000	11.100000	15.100000	0.2500	55.100000	33.100000	9.100000	1.900000	0.800000
28	0	60	28.360000	32.300000	64.430000	30.760000	71.380000	43.210000	28.170000	1.530000	0.920000	4.590000	1.530000	2.690000	8.250000	78.720000	322.390000	0.016090	0.032179	0.024872	2.323333	2.280577	4.830000	5.040	156.000000	0.4680	92.90	31.00	333.000000	12.300000	201.000000	10.500000	11.400000	0.2100	54.200000	35.800000	7.500000	1.700000	0.800000
29	0	55	29.600000	34.070000	99.960000	41.950000	82.330000	45.490000	36.840000	1.230000	1.620000	5.280000	1.250000	3.580000	5.330000	88.480000	389.350000	0.016410	0.032821	0.025128	2.316667	2.276923	5.850000	5.400	165.000000	0.4930	91.30	30.60	335.000000	13.200000	260.000000	11.100000	13.500000	0.2900	55.700000	30.100000	10.400000	2.600000	1.200000
30	0	56	22.680000	16.930000	102.980000	30.430000	73.750000	47.420000	26.330000	1.800000	0.690000	5.410000	2.070000	2.850000	3.660000	68.160000	245.800000	0.016731	0.033462	0.025385	2.310000	2.273269	7.780000	4.710	150.000000	0.4490	95.30	31.80	334.000000	12.600000	308.000000	10.400000	12.100000	0.3200	50.400000	38.000000	9.400000	1.800000	0.400000
31	0	59	39.700000	28.170000	118.780000	22.640000	76.720000	43.770000	32.950000	1.330000	0.780000	5.430000	1.730000	3.430000	5.990000	96.640000	278.500000	0.017051	0.034103	0.025641	2.303333	2.269615	6.200000	5.600	166.000000	0.4820	86.10	29.60	344.000000	13.100000	187.000000	10.200000	12.100000	0.1900	50.300000	42.100000	6.800000	0.500000	0.300000
32	0	58	20.840000	23.660000	83.130000	150.090000	71.550000	44.960000	26.590000	1.690000	4.850000	6.780000	1.770000	4.500000	7.520000	96.310000	323.780000	0.017372	0.034744	0.025897	2.296667	2.265962	9.890000	4.600	152.000000	0.4320	93.90	33.00	352.000000	12.400000	273.000000	9.800000	10.600000	0.2700	39.100000	53.000000	5.900000	1.300000	0.700000
33	0	32	30.230000	39.730000	67.520000	27.260000	79.210000	46.960000	32.250000	1.460000	2.100000	6.060000	1.350000	4.100000	4.350000	73.930000	389.170000	0.017692	0.035385	0.026154	2.290000	2.262308	4.960000	5.410	161.000000	0.4730	87.40	29.80	340.000000	12.100000	253.000000	10.800000	13.500000	0.2700	56.800000	33.100000	8.100000	1.400000	0.600000
34	0	35	20.930000	37.670000	64.630000	46.950000	75.520000	46.230000	29.290000	1.580000	3.510000	4.770000	0.880000	3.290000	4.090000	100.130000	378.250000	0.018013	0.036026	0.026410	2.283333	2.258654	6.710000	5.080	158.000000	0.4630	91.10	31.10	341.000000	12.300000	210.000000	10.400000	12.600000	0.2200	34.100000	52.500000	7.900000	4.900000	0.600000
35	1	38	18.810000	12.550000	52.550000	16.030000	81.270000	48.170000	33.100000	1.460000	1.020000	5.300000	1.460000	3.480000	8.480000	77.190000	366.380000	0.018333	0.036667	0.026667	2.276667	2.255000	6.930000	5.040	145.000000	0.4300	85.30	28.80	337.000000	13.000000	272.000000	10.100000	11.700000	0.2700	55.400000	35.100000	6.900000	2.200000	0.400000
36	1	47	26.580000	16.030000	71.790000	30.680000	82.170000	42.870000	39.300000	1.090000	1.430000	5.760000	1.240000	4.020000	3.480000	63.550000	243.690000	0.018654	0.037308	0.026923	2.270000	2.251346	7.360000	4.530	134.900000	0.3960	87.30	29.80	341.000000	13.300000	335.000000	8.500000	15.700000	0.2830	51.600000	35.000000	8.000000	4.600000	0.800000
37	1	36	17.990000	10.110000	55.820000	26.140000	75.470000	43.180000	32.290000	1.340000	1.530000	5.700000	1.270000	3.920000	6.480000	69.540000	311.170000	0.018974	0.037949	0.027179	2.263333	2.247692	7.480000	4.020	129.000000	0.3750	93.30	32.10	344.000000	12.300000	274.000000	10.000000	11.100000	0.2700	57.600000	35.000000	6.000000	0.900000	0.500000
38	1	45	20.580000	11.250000	60.210000	11.620000	75.650000	43.980000	31.670000	1.390000	0.940000	4.630000	1.650000	2.750000	5.380000	73.600000	227.930000	0.019295	0.038590	0.027436	2.256667	2.244038	6.120000	4.510	135.600000	0.4050	89.80	30.00	335.000000	12.700000	213.000000	8.700000	16.500000	0.1850	50.000000	40.900000	7.900000	0.600000	0.500000
39	1	53	20.470000	10.760000	132.270000	21.110000	76.450000	46.280000	30.170000	1.530000	1.470000	6.650000	1.660000	4.370000	3.060000	65.440000	225.150000	0.019615	0.039231	0.027692	2.250000	2.240385	5.420000	4.460	134.000000	0.4080	91.50	30.00	328.000000	13.100000	402.000000	9.700000	10.500000	0.3900	47.800000	43.200000	7.000000	1.300000	0.700000

	GLU
0	6.06
1	5.39
2	5.59
3	4.30
4	5.42
5	5.97
6	5.11
7	5.94
8	5.66
9	5.48
10	4.44
11	5.48
12	5.32
13	5.08
14	4.63
15	4.92
16	7.08
17	4.66
18	6.58
19	4.29
20	4.55
21	5.16
22	5.13
23	4.37
24	5.17
25	4.64
26	5.82
27	6.29
28	5.10
29	5.41
30	5.48
31	6.44
32	5.53
33	5.31
34	5.52
35	4.70
36	5.13
37	6.06
38	5.41
39	5.82